1

Mainstreaming International Trade into National Development Strategy

Regional Trade Openness Index, Income Disparity and Poverty

- An Experiment with Indian Data

Sugata Marjit and Saibal Kar

Centre for Studies in Social Sciences, Calcutta

July 2008

2

Introduction

• Trade affects regional income of a geographically large developing country

• Egger, Huber and Pfaffermayr (2005) deals with trade openness of EUs and regional disparity (based on available regional trade data)

• Absence of regional/provincial trade data

• Lack of proper indicator of regional trade openness, and relation between openness and poverty, regional income differences, etc.

3

Approach, Questions, Observations

• How could one deal with the issue of trade openness and poverty?

• Two ways to approach the issue: Macro and Micro

•This study is a Macro exercise -- devise a holistic measure of trade openness (TOI) across regions – use that openness index to relate with regional disparity in income, regional indices of poverty and industrial employment

•Most Important observations include positive impact of TOI on urban HCR and rural inequality

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Relevant Studies and Main Outcomes of this Study

• The relevant literature discusses within country openness and the regional trade openness index created here is novel. Previous attempts at convergence tests via openness includes Maiti (2004) and Marjit and Maiti (2006), Purfield (2006), Topalova (2005), etc.

•States with traditional emphasis on production of commodities that are intrinsically import competing in nature have suffered an income loss over these years.

•provinces that retained larger share of production in the export category faced improvement in their PCNSDP

•Industrial employment showed increasing trends till the immediate pre-reform period after which it falls at an increasing rate

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ROI – Initial Methodology and Improvements

• Unavailability of trade data by regions

• Devise a proxy for ‘trade’ by using production (export and import competing commodities) data at the state level.

•DGCIS is the source of trade data according to HS classification

• ASI is the source of State industrial data according to NIC classification

•Since ASI and DGCIS use different definitions, we reclassify and merge comparable data at the 2-digit level

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Methodology --continued

•For a specific state, the level of output (i.e. sum of industrial and agricultural output) has been linked to all-India trade figures to get an approximate indicator of how much ‘open’ a particular state is.

• We exclude service sector due to lack of production or trade data

•Instead of the arbitrary 0.5 as the share of both exports and imports used previously –export goods share ( )

And ( ) as the import goods share of each industry in total export or import --- used as weights to obtain the weighted TOI.

t

itit X

Xx

t

itit M

Mm

7

kmt

kmt

kxt

kxt

kt RsRsROI

~1

The new TOI is then written as

(the export performance rank) and the inverse (the import competing performance rank)

is share of exportable production of k-th state at t-th period

is share of importable production of k-th state at t-th period

kmtR

~kmtR

kxts

kmts

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Econometric Model

The model follows GMM (Generalized Method of Moments) specifications to get rid of state-specific factors (equation below)

itittiitititit DZXYY ln)1(ln

itYln

The above term is used as the instrument and Then substituted byAs a better Instrument 2_ln itGFCFPC

Table 1

Table 2

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GMM RESULTS (TABLE 1)

Dependent variable:

R e g r e s s o r s ( 1 ) ( 2 ) ( 3 ) ( 4 )

)2(ln tiPCNSDP 0 . 5 1 7 * * * 0 . 5 1 7 2 * * * 0 . 5 1 6 * * * 0 . 5 1 5 * * *

itXCI - 0 . 0 0 8

itMCI - 0 . 0 0 2 1

itROI 1 - 0 . 0 0 8 4

itROI 2 - 0 . 0 0 9 1

itGEPC _ 1 . 3 0 2 1 . 2 9 6 1 . 3 0 2 1 . 3

iD 0 . 0 0 4 0 . 0 0 4 9 0 . 0 0 5 5 0 . 0 0 5 6

itRD 7 9 . 4 * 8 1 . 0 3 8 1 . 5 * 8 1 . 2 *

itELC 0 . 0 0 0 1 4 0 . 0 0 0 1 4 0 . 0 0 0 1 0 . 0 0 0 1 3

itLIT 0 . 0 0 5 * * * 0 . 0 0 5 * * * 0 . 0 0 5 * * * 0 . 0 0 5 1 * * *

I n s t r u m e n t a l v a r i a b l e s

)4(ln tiPCNSDP

a n d f u r t h e r l a g s )4(ln tiPCNSDP

a n d f u r t h e r l a g s )4(ln tiPCNSDP

a n d f u r t h e r l a g s )4(ln tiPCNSDP

a n d f u r t h e r l a g s

W a l d c h i 2 ( 7 ) 1 1 3 3 . 8 3 5 8 . 3 4 3 1 4 . 9 7 3 0 7 . 8 5 A R ( 1 ) - 1 . 6 2 - 1 . 6 2 - 1 . 6 3 - 1 . 6 4

1lnlnln ititit PCNSDPPCNSDPPCNSDP

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GMM RESULTS (TABLE 2)

Dependent variable: 1lnlnln ititit PCNSDPPCNSDPPCNSDPR e g r e s s o r s ( 1 ) ( 2 ) ( 3 ) ( 4 )

)2(ln tiPCNSDP 0 . 5 4 2 * * * 0 . 5 5 5 4 * * * 0 . 5 4 6 6 * * * 0 . 5 4 8 7 * * *

itXCI - 0 . 0 1 4 5 * *

itMCI - 0 . 0 0 1 5

itROI 1 - 0 . 0 1 2 * *

itROI 2 - 0 . 0 0 9 3 * *

itGEPC _ 1 . 3 * * * 1 . 2 8 * * * 1 . 3 0 7 * * * 1 . 2 8 8 * * *

iD 0 . 0 0 2 4 0 . 0 0 2 8 0 . 0 0 2 9 0 . 0 0 2 9 9

itRD 1 1 3 . 4 3 * * * 1 1 9 . 5 6 * * * 1 1 9 . 0 3 8 * * * 1 1 6 . 0 6 * * *

itELC 0 . 0 0 0 0 9 0 . 0 0 0 0 8 0 . 0 0 0 0 7 7 0 . 0 0 0 0 7 8

itLIT 0 . 0 0 3 7 * * 0 . 0 0 3 7 * * 0 . 0 0 3 7 * * 0 . 0 0 3 8 * *

I n s t r u m e n t a l v a r i a b l e s a n d f u r t h e r l a g s a n d f u r t h e r l a g s a n d f u r t h e r l a g s a n d f u r t h e r l a g s

W a l d c h i 2 ( 7 ) 7 0 8 . 6 4 4 3 8 . 5 2 5 2 7 . 0 6 4 0 9 . 8 1

A R ( 1 ) - 1 . 7 1 - 1 . 6 2 - 1 . 6 7 - 1 . 6 5

2_ln itGFCFPC 2_ln itGFCFPC 2_ln itGFCFPC 2_ln itGFCFPC

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Fig. 7 Correlation between Regional TOI and Growth of Workers across Industries (SIC)

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1981-82

1982-83

1983-84

1984-85

1985-86

1986-87

1987-88

1988-89

1989-90

1990-91

1991-92

1992-93

1993-94

1994-95

1995-96

1996-97

1997-98

Years

Co

rre

lati

on

Co

eff

icie

nt

SIC20-21 SIC22 SIC23 SIC25 SIC26

SIC27 SIC28 SIC29 SIC30 SIC33

SIC35-36 SIC37 Poly. (SIC20-21) Poly. (SIC37)

Relation between TOI and Industrial Employment across SIC

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Relationship between TOI and Urban-Rural HCR

Fig. 11 Correlation Coefficient between Urban and Rural HCR and TOI

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1983 -84

1986 -87

1987 -88

1988 -89

1989 -90

1990 -91

1991 -92

1992 -93

1993 -94

1994 -95

1995 -96

1996 -97

1997 -98

1999 -00

Years

Co

rrela

tio

n C

oeff

icie

nt

Urban HCR Rural HCR Linear (Rural HCR) Linear (Urban HCR)

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Openness Index: Methodology (contd.)Relationship between TOI and Urban-Rural Poverty Gap

Fig. 12 Correlation between TOI and Urban and Rural Poverty Gap

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1983 -84

1986 -87

1987 -88

1988 -89

1989 -90

1990 -91

1991 -92

1992 -93

1993 -94

1994 -95

1995 -96

1996 -97

1997 -98

1999 -00

Years

Co

rre

lati

on

Co

eff

icie

nt

Urban PG Rural PG Linear (Urban PG) Linear (Rural PG)

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Relationship between TOI and Urban-Rural SQ Poverty Gap

Fig. 13 Correlation between TOI and Urban and Rural Squared Poverty Gap

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1983 -84

1986 -87

1987 -88

1988 -89

1989 -90

1990 -91

1991 -92

1992 -93

1993 -94

1994 -95

1995 -96

1996 -97

1997 -98

1999 -00

Years

Co

rre

lati

on

Co

eff

icie

nt

Urban SPG Rural SPG Linear (Urban SPG) Linear (Rural SPG)

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Relationship Between Openness and InterregionalRelationship between TOI and Urban-Rural Gini

Fig. 14 Correlation between TOI and Urban and Rural Gini

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1983 -84

1986 -87

1987 -88

1988 -89

1989 -90

1990 -91

1991 -92

1992 -93

1993 -94

1994 -95

1995 -96

1996 -97

1997 -98

1999 -00

Years

Co

rrel

atio

n C

oef

fici

ents

Urban GINI Rural GINI Linear (Urban GINI) Linear (Rural GINI)

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Primary Surveys and Case Studies

This is the first disaggregated (state-level) measure of TOI

Within state dis-aggregation is unobservable due to lack of data (Topalova, 2005, looks at import competition at the districts only, NOT TOI)

Thus, identified certain areas with high trade related activities for micro implications of trade

Case studies from West Bengal based on primary survey Subsequently, two specific case studies from

Maharashtra and Gujarat – more akin to our previous and continuing work on Trade in Informal sector products and poverty (Kar and Marjit, IREF, 2008, forthcoming; Marjit and Kar, 2007, PEP Working Paper).

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The effect of international trade on low wage workers in West Bengal

subtle changes at the grass root level within a country is often not captured

Weavers of Santipur-Phulia (Nadia), Import Competing production in Durgapur-Asansol (Burdwan), industrial belts of Kolkata-Hoogly, Labor migration from Sagardighi (Murshidabad)

Five small scale exporting firms selected from all three areas (except Sagardighi)

150 employees were randomly selected and interviewed with the help of structured questionnaires

In Sagardighi 50 labour households were selected

Trade, Development and Social ChangeCase Studies from West Bengal

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SANTIPUR-PHULIA Before 1991, Textile firms were many in number few cooperatives but major business was

controlled by a few traders Major demand from local and Kolkata markets Firm Infrastructure was poor, low prices to cater

widely Weavers were paid low wages Limited formal credit facilities

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Santipur..contd..

• Since 1991, some hurdles removed mainly via access to information about markets in other metros and overseas.

• Producers’ dependence on middlemen substantially reduced, able to market directly, take part in trade fairs etc.

• Tables show changes in conditions of employment and level of living within last decade

• Textile producers maintain two different scales and technologies of operation and expanding on both

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Santipur..contd..Conditions of Employment Categories No. of Respondents

Contractual 48 Casual 02

Nature of current employment

Others - Increased 49 Decreased - Change in wage rate Unchanged 01 Increased 45 Decreased - Change in other benefits Unchanged 05

Need More Skill 46 Need less skill - Change in nature of job

Unchanged 04 Increased 45 Decreased - Uncertainty Unchanged 05

Better 46 Worse - Employer employee relation

Unchanged 04

Table1.Changes in employment conditions

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Santipur..contd..Table2.Changes in living conditions

Level of Living Categories No. of Respondents

Increased 48 Decreased - Food Consumption Unchanged 02 Improved 45

Deteriorated 02 Housing Unchanged 03

More Affordable 40 Less Affordable - Children’s Education

Unchanged 10 Increased - Decreased 45 Indebtedness Unchanged 05

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Case Study from Durgapur• Durgapur was a booming industrial town till the late

eighties • In the nineties, large PSU’s and millions of ancillary

industries based on them went out of business• Industrial Resurgence is very recent – in the span of

last 3-5 years, mainly driven by demand for steel in China

• The ailing ancillary industries have come back to life

• 5 such companies surveyed with response from 50 employees -- conditions in the following tables

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Table 3. Employment conditions (Durgapur)Durgapur…contd….

Conditions of Employment Categories No. of Respondents

Contractual 45 Casual 05

Nature of current employment

Others - Increased 48 Decreased - Change in wage rate Unchanged 02 Increased 40 Decreased - Change in other benefits Unchanged 10

Need More Skill 40 Need less skill - Change in nature of job

Unchanged 10 Increased - Decreased 40 Uncertainty Unchanged 10

Better 45 Worse - Employer employee relation

Unchanged 05

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Durgapur…contd….Table 4. LIving conditions (Durgapur)

Level of Living Categories No. of Respondents

Increased 48 Decreased - Food Consumption Unchanged 02 Improved 45

Deteriorated Housing Unchanged 05

More Affordable 45 Less Affordable - Children’s Education

Unchanged 05 Increased - Decreased 42 Indebtedness Unchanged 08

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Sagardighi (Murshidabad)

• Murshidabad is one of the poorest districts in West Bengal and recently categorized under A category (severe) in terms of concentration of minorities and the gaps that exist in per capita basic amenities compared to the national averages.

• Only 38% of people live in Pucca house, general work participation is 39%, 24% houses with electricity, 23% houses with in-house toilet facilities, 92% students drop out before 8th Standard

• High degree of migration for work from all the villages, including Sagardighi (Table 5)

• Essentially, (not formally) linking labor mobility with high activities in real estate, an outcome of capital inflow – a possible future research agenda across religious communities, gender and income classes

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Sagardighi…contd..Table 5: Migration for Work:Community wise District Averages (%) (HH Survey)

Muslim Non-Muslim

Short Term 79.09 62.07

Duration

Long Term 20.91 37.93

Within District (Village) 3.60 3.45

Within District (Town) 5.41 27.59

Within State (Village) 4.50 6.90

Within State (Town) 32.43 31.03

Outside State (Village) 2.70 0.0

Outside State (Town) 49.55 27.59

Place of work

Abroad 1.80 3.45

Professional Work 4.50 25.0

Administrative Work 0.90 7.14

Clerical Work 0.0 3.57

Sales Work 7.21 10.71

Farmer 7.21 0.0

Transport and labourers 61.26 28.57

Student 1.80 10.71

Reasons for migration

Others 17.12 14.29

Repatriation Household 84.40 88.46

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Primary Surveys and Case Studies

Leather Products (Handbags) Industry of Dharavi Since the Dharavi’s re-development plan most leather

exporting associations in the area are shifting the Rs 300-crore industry to Bhiwandi

International buyers sometimes reject Dharavi’s products as they have a tendency of not being consistent in quality.

International leather agents demand to work with only those exporters who can offer quality products on a large scale through mechanized production

Mumbai has lost its prowess in the leather business to cities such as Kolkata, Chennai and Kanpur

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Dharavi…contd..• Shift in prosperity to other locations was the proximity

of abattoirs and tanneries to production centres• Notably, this leather industry by itself may still be

profitable, but yielded to high land prices in the region -- another possible outcome of high intensity of openness, capital inflow in retail sectors and real estate, growth of urban service sector – once again, not formally tested, but relevant evidences for research in openness, growth and displacement

• much of the work has little official status and lacks professionalism

• A final factor pushing most entrepreneurs is the access to credit

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Paper Product Industry of Surat

• Demand Driven --- • Local demand increased, as the literacy rate picked up.• Export market opened up for Indian made notebooks and

all types of writing books etc.• Indian manufacturers were accepting small orders,

whereas Chinese manufacturers wanted huge orders to feed their big capacities

• The new Linomatic Ruling machine’s one-day production equalled to 10 hand-ruling machines.

• One Linomatic machine operated by 2 people displaced 10 hand ruling machine operators

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Several other issues for future research, some already identified -- aggregate evidence for the general trends and individual but linked case studies for more micro level formalization for which secondary data is not

available.

1. Transition from regional trade openness to growth to poverty reduction – an ambitious project given the paucity of Indian data

2. Trade, firm structures and labor mobility – specializations and vanishing occupations – Theory and application with Indian data